ADon’t Derail Your ADMSImplementation With Bad Data

Advanced distribution management systems
(ADMS), which include outage management and distribution
management, are keys to automating utility processes and
integrating systems. These mission critical systems and the
systems with which they interface give results only as good
as the data that goes in.

While perfection is unattainable, it is possible to be
useful with values like 60 percent data quality, but the aim
should be much higher. In addition, the need to review data
never ends.

An ADMS is the center point of data flowing between
multiple systems (Figure 1), including geographic information
systems (GIS), customer information systems (CIS), SCADA,
interactive voice response (IVR) and advanced metering
infrastructure (AMI). The GIS data, which is this article’s
focus, is particularly critical because it inputs data on which
ADMS depends for its mission critical function. Inaccuracies
in the ADMS can propagate to the other integrated systems,
multiplying the impact of poor data quality.

Some small amount of inaccuracy is unavoidable, but
it must be minimal. Ramifications of bad data can include
wrong outage predictions to overloads to even crew’s safety.
Dispatchers who see significant errors in the system will
likely block the system from going live until data improves.
If it is already live, they will feel crew safety is too important
to use an untrusted system and will rely instead on what they
trust (like paper maps) and not the system.

An ADMS often has data requirements stricter than thesource GIS system it came from because the data is useddifferently. If inaccurate data doesn’t impact GIS usage,issues likely will remain until another system reveals them.For example, certain topology and energization factors thatweren’t important in the GIS are important in the ADMS.If extra, missing or incompletely defined connections ormismatched phasing exist in the ADMS, deenergization orlooping appears in feeders and customers may not be properlyrestored in the ADMS. These kinds of problems potentiallygo unnoticed when isolated in a GIS.

Following is a short list of problems that can be caused by bad
or missing data in an ADMS:
• Incorrect or lack of energization
• Incorrect reports and indices
• Outages not predicting correctly
• SCADA devices with wrong measurements or lack of control
• Sluggish performance
• Missing or incorrect device details
• Overloaded circuits
• Incorrect switching plans
• Proliferation of incorrect information out from the ADMS
• Crew members injuries

Review and Correct Early

Best practice is to correct data issues as early as possible
because considerable effort and duration is needed. Some
issues are like an onion, in that removing one layer reveals
more. Rewards are reaped in later project phases, especially
during testing, when data issues are resolved early. Data model
fixes made late in the project often result in time-consuming
ramifications, like the need for extensive rework to already
created test cases or training materials. When significant data
model fixes are implemented during testing, testing progress
may pause while changes are made to large numbers of test
cases. Data changes can even result in invalidating test results,
requiring re-execution of previously passed tests.

Another reason to review and fix the data model early is
to maintain the users’ positive opinion of the system.

Fixes should be made before functional testing or
training occurs, otherwise, a negative opinion can form early
and be hard to change. Users with negative feelings about the
new system can undermine user confidence in the system and
impact motivation, as well as morale.

Ross Shaich has more
than 18 years’ experience
in ADMS implementation
and support of large-scale
enterprise projects. He has
served as subject matter
expert, test lead, functional
lead, test designer and
project manager. He holds
a master’s degree in
project management and
is a certifed PMP. Reach
him at rshaich@uisol.com